Public Social Distance Monitoring System Using Object Detection YOLO Deep Learning Algorithm

Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best...

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Vydané v:	SN computer science Ročník 4; číslo 6; s. 718
Hlavní autori:	R, Vijayan, V, Mareeswari, Pople, Vedant
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Nature Singapore 01.11.2023 Springer Nature B.V
Predmet:	Accuracy Algorithms Applications programs Classification Comparative analysis Computer Imaging Computer Science Computer Systems Organization and Communication Networks Computer vision Coronaviruses COVID-19 Data Structures and Information Theory Deep learning Disease prevention Disease transmission Energy consumption Frames (data processing) Frames per second Graphics processing units Information Systems and Communication Service Machine learning Mobile computing Monitoring Monitoring systems Neural networks New technology Object recognition Original Research Pandemics Pattern Recognition and Graphics Research Trends in Computational Intelligence Security systems Social distancing Software Engineering/Programming and Operating Systems Video data Violations Vision YOLO Deep learning Computer vision Object classification Social monitoring Object detection
ISSN:	2661-8907, 2662-995X, 2661-8907
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Abstract	Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places. Graphical Abstract
AbstractList	Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places. Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places. Graphical Abstract
ArticleNumber	718
Author	V, Mareeswari Pople, Vedant R, Vijayan
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Cites_doi	10.1007/s10462-020-09825-6 10.1016/j.jiph.2020.03.019 10.1088/1742-6596/1679/4/042022 10.1109/ACCESS.2019.2961959 10.1088/1742-6596/1916/1/012039 10.1007/s13042-020-01220-5 10.1109/TPAMI.2017.2687462 10.1007/s11554-022-01203-5 10.1007/s11554-021-01070-6 10.1088/1757-899X/533/1/012056 10.1109/ACCESS.2020.3018140 10.1177/1729881420936062 10.1186/s40537-021-00434-w 10.1109/JIOT.2017.2787779 10.1016/j.scs.2020.102582 10.3390/cryptography6020016 10.1155/2022/2103975 10.1371/journal.pone.0247440 10.1080/21642583.2020.1824132 10.1016/j.ins.2020.02.067 10.1109/TPAMI.2016.2577031 10.4218/etrij.15.0114.0629 10.3390/s20174938 10.1007/s00521-023-08556-3 10.3390/app10093079 10.1109/TNNLS.2018.2876865 10.3390/s19112508 10.1007/s10586-017-0968-3 10.1109/ACCESS.2021.3094720 10.1109/ICACCS48705.2020.9074315 10.1109/ICCMC51019.2021.9418478 10.1109/CRV.2018.00023 10.1109/ICCSCE50387.2020.9204934 10.1109/ISC2.2017.8090864 10.1109/CVPR.2016.91
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SubjectTerms	Accuracy Algorithms Applications programs Classification Comparative analysis Computer Imaging Computer Science Computer Systems Organization and Communication Networks Computer vision Coronaviruses COVID-19 Data Structures and Information Theory Deep learning Disease prevention Disease transmission Energy consumption Frames (data processing) Frames per second Graphics processing units Information Systems and Communication Service Machine learning Mobile computing Monitoring Monitoring systems Neural networks New technology Object recognition Original Research Pandemics Pattern Recognition and Graphics Research Trends in Computational Intelligence Security systems Social distancing Software Engineering/Programming and Operating Systems Video data Violations Vision
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Title	Public Social Distance Monitoring System Using Object Detection YOLO Deep Learning Algorithm
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